AI-powered tools revolutionizing field service management in telecom

AI technology in telecom is expected to advance from $3.34 billion in 2024 to $58.74 billion by 2032. In recent years there has been an increase in the adoption of AI tools by telecom providers to manage their workforce more effectively, foresee problems before they occur, meet strict regulatory requirements, and bring automation and efficiency to their field service.

There are major implications in terms of cost when it comes to field service management deficiencies for telecom operators. Despite the fact that most telecom operators have enhanced their field service management capabilities, they still employ a reactive maintenance strategy and conventional scheduling, which makes it difficult to run the network without interruptions and with increasing costs. Among the most significant obstacles are:

• Issues in staff scheduling: managing people in the telecom sector is one of the hardest tasks. In a recent article by BCG Platinion, it was revealed that 70% of telecom operators face high operational costs and poor service delivery due to scheduling mistakes.
• Reactive maintenance cause service interrups: most telecom companies operate in a break-fix model that requires problem-solving only after its occurrence. Studies reveal that AI-driven predictive maintenance offers a 30% reduction in downtime and 20% longer asset life which translates into fewer costly emergency repairs.
• Complexity of regulatory compliance: telecom operators are confronted with a rising number of rules and documentation standards such as FCC's Broadband Data Collection initiative in the US or the EU Digital Decade framework. Manually monitoring compliance requires great effort and is error-prone because it exposes businesses to fraud, legal actions, and fine risks.
• Customer expectations: since telecom services have become essential to daily functioning, consumers demand prompt solutions to outages. According to PwC's survey, 85% of consumers choose their telecom provider based on how quickly the company resolves issues.

The systems enabled by AI enhance automation, predictive analysis, and real-time data processing hence improving service delivery, improving the efficiency of the field teams, and avoiding certain problems before they become problems. Some of the most relevant improvements in AI for FSM include:

• Predictive maintenance for increasing the network reliability: the traditional approaches to maintenance are fixed interval or breakdown maintenance which results in costly downtime and inadequate use of resources.
  AI approach to maintenance utilizes real-time sensor data, IoT devices, and historical trends to catch the signs of equipment failure before they become critical. It uses machine learning models to identify the potential issues that can help in scheduling the maintenance at the right time, and in a cost-effective manner to increase the lifespan of the infrastructure, with less likelihood of unexpected service disruptions and expenses.
• Improved technician dispatch and scheduling: it is possible to get an AI-enabled FSM system that can help dispatch the right technician to the right job at the right time. AI optimizes dispatching based on traffic conditions, geospatial information, and past job performance to determine when and where to dispatch technicians based on their availability, location, and expertise. This reduces travel time and costs, fuel and site visits, solves issues faster, improves customer satisfaction, and conformity with SLAs.
• Automated compliance monitoring: the telecom industry is one of the most regulated industries requiring strict controls on data protection and network infrastructure safety.
• AI-compliance system uses NLP to analyze and assess potential non-compliance from data, validate it, and analyze regulations. The generation of reports and audits, and the conduct of trials through AI ensures there is little or no human interjection, thus avoiding costly compliance issues.

As the use of AI technology in the telecommunications industry continues to rise, industry leaders are now focusing on the standardization and integration of the technology to realize its full potential. The telecom networks are being readied to embrace AI in a seamless manner with the help of AI frameworks. Some of the future’s noteworthy developments include:

• Self-healing networks: in the future, telecom network automation will be intensified by AI to enable networks to identify and solve problems without the need for a human.
• AI-powered digital twins: these digital representations of telecom networks will enable operators to forecast failures, improve resource allocation, and replicate real-world situations.
• Augmented reality (AR) for field technicians: technicians benefit from decreased downtime and higher fix rates through AI-enabled AR apps that offer visual overlays, detailed troubleshooting guidance, and remote assistance.

AI-based field service management is not just a new trend, but the foundation of a new approach that enhances service delivery, optimizes the workforce, and ensures proper compliance. While telecom operators are redefining field service management in a highly connected digital environment with AI, best practices are also being developed to facilitate its adoption. AI-based FSM is no longer an option; it’s a necessity and the future lies with innovators in this space.